Nitrogen Time of Application Impact on Productivity, Water Use Efficiency and Agronomic Efficiency of Maize in a Semi-arid Environment
Bibliographic record
Abstract
Water is one of the most important limiting factor of rainfed continuous maize (Zea mays L.) cropping systems in northwest of China. A three continuous year field experiments were conducted to study the influence of different nitrogen time of application on grain yield and water use efficiency of maize (Zea mays L.) in the Western Loess plateau. The experiment was laid in a randomized complete block design with two treatments and three replicates. Treatments were; (one-third application of N at sowing + two-third application at pre-flowering) and (one-third application of N at sowing + one-third pre-flowering + one-third at milking) as T1 and T2 respectively. The results showed that, T1 significantly increased grain yield by 9% in 2014 and 2016; and WUE by 11% in 2016 compared to T2. T1 increased AE by 43% compared to T2. Our results indicate that ⅓ application of Nitrogen at sowing and ⅔ application of Nitrogen at pre–flowering (T1) for maize is more appropriate for sustainable maize production in terms of satisfactory grain-N recoveries and low environmental losses of N fertilizer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".